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mcp-knowledge

by io.github.getgapup

ai_governance_full_report_async

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Audit EU AI Act complet (Règlement UE 2024/1689) — implémentation native audit-grade. Classifie le système IA selon les 4 tiers de risque (unacceptable/high_risk/limited_risk/minimal_risk/gpai) sur la base de l'Annexe III et de l'Article 5. Produit : (1) classification tier + justification + articles applicables, (2) checklist conformité Articles 9-15 + 50 + 53-55, (3) gaps documentation Annexe IV, (4) mapping ISO 42001, (5) deadlines EU AI Act 2025-2029, (6) estimation coût et effort, (7) top 10 recommandations P0/P1/P2. Retourne immédiatement (<300ms) un job_id. Poller avec ai_governance_full_report_result(job_id) après eta_seconds (~90s). Cache 7 jours pour inputs identiques. Async tool — register a webhook via webhooks_manage(register, url, [job.completed]) to receive callbacks instead of polling. Faster + lighter. DISCLAIMER : non substitutif à un avis juridique professionnel.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_sizeNoTaille entreprise : startup (≤50), smb (51-250), mid (251-1000), large (1001-5000), enterprise (>5000)
data_sourcesNoSources de données utilisées par le système IA
affected_personsNoCatégories de personnes affectées par les décisions du système (ex: candidats, employés, clients)
geographic_scopeNoZones géographiques de déploiement (ex: 'EU', 'France', 'Global')
intended_purposeYesFinalité prévue du système IA : à quoi sert-il concrètement
deployment_contextNoContexte de déploiement : interne (usage employés), public, B2B, B2C
ai_system_descriptionYesDescription détaillée du système IA : ce qu'il fait, comment il fonctionne, quelles décisions il prend

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesIdentifiant unique du job — passer à ai_governance_full_report_result
statusYes
eta_secondsYesDurée estimée avant disponibilité du résultat
submitted_atYesTimestamp ISO-8601 de soumission

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses key behaviors: returns job_id in <300ms, caches 7 days, async with polling or webhook, includes disclaimer. However, annotations state readOnlyHint=true while description implies a write operation (creating a job), creating a minor inconsistency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured and concise: front-loaded purpose, enumerated outputs, timing, cache, and alternative. Every sentence adds value, no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Fully explains tool's purpose, output, async nature, caching, and integration options. Given complexity (7 params, output schema exists), description covers all necessary context for invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with param descriptions. Description does not add significant detail beyond schema, but the baseline of 3 applies as schema handles param semantics adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool audits EU AI Act, classifies risk tiers, and produces 7 specific outputs. It distinguishes itself from siblings like ai_governance_full_report_result and ai_governance_pilot by being an async submit that returns a job_id.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly advises when to use (for full EU AI Act compliance) and provides alternatives: polling with ai_governance_full_report_result or using webhooks via webhooks_manage. Mentions faster+lighter webhook approach.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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